Multi-element systems such as a power plant can involve the complex integration of multiple elements cooperatively performing a variety of tasks in order to attain a desired system performance or system output. Given the complexity of many such systems, it is not surprising that the task of monitoring the performance of a complex system can be difficult. Often times, especially in systems like a power plant, the paramount reason for monitoring is to detect conditions that could lead to a system failure. The objective, of course, is to detect the conditions early enough so that they can be ameliorated before a failure occurs.
One monitoring technique in such an environment is to use multiple sensors to detect and monitor the operation or performance of a complex system. Sensor-based monitoring can be used for efficient control in a variety of industrial settings, including power generating systems, manufacturing processes, and a host of other industrial operations involving the coordinated functioning of large-scale, multi-component systems. Indeed, sensor-based monitoring can be advantageously employed in virtually any environment in which various system-specific parameters need to be monitored over time and for a variety of conditions.
Sensor-based monitoring stems from the view that a power plant or other complex system is a multiple-input, multiple output (MIMO) system. According to this view multiple inputs, or process drivers, are supplied to the MIMO system, and based on the inputs, the MIMO system generates output. In the context of a power generation system, for example, the process drivers include gas and air, with which various output are produced. The outputs include not only power, but also other outputs that can be measured, such as temperature, pressure, and vibration. Both the inputs and the resulting outputs can be monitored with the right kind of sensors appropriately positioned relative to the system.
Related to sensor-based monitoring generally, new techniques have been advanced in recent decades for detecting system faults using model-based fault detection. The underlying idea of many such techniques it that of analytical redundancy as opposed to physical redundancy. The latter approach utilizes redundant sensors, whereas the former approach utilizes signals representing estimated values generated in accordance with a mathematical model of the system. The estimated values are compared to actual measurements obtained by sensors, giving rise to residuals—the difference (typically an absolute value) between the estimated and actual values generated by the sensors. The residual values, in turn, are compared with threshold values. A system fault is indicated if a residual lies outside an acceptable range of values. Thus, system diagnosis and fault detection is based on a comparison of prior information represented by the system model with actual measurements. The model itself can be determined using, for example, statistical methods such as regression.
Owing to system complexity, however, the accuracy of even these more advanced techniques can be reduced when less than all of the inputs are known or accurately modeled. For example, with respect to a turbine engine or generator, humidity can be a very important input into the system, but in many circumstances, is not measured. Using only partial information in a regression or other model-building procedure can produce a less accurate model.
One solution to the problem has been to utilize known or estimated correlations among sensors that measure an output of the MIMO system. For example, in monitoring a combustion turbine engine, all N-blade path temperature sensors are sufficiently correlated so that one such sensor can be used to predict the value of another. Different techniques for inferential sensing have been proposed and tested. These include auto-associative neural networks, non-parametric kernel regression, multivariate state regression, and support vector regression. According to these techniques, one correlated sensor is a MIMO system output which is regressed on the other correlated sensors whose sensor values represent inputs.
One persistent problem, even with these more advanced techniques, however, is the problem of spillover. Spillover can occur if any of the input correlated sensors is faulty or otherwise inaccurate. The result is that the resulting model and system monitoring capability are reduced accordingly. Current monitoring devices and techniques have not adequately addressed this problem.